VerbAgg: Verbal Aggression item responses

Description Format Source References Examples

Description

These are the item responses to a questionaire on verbal aggression. These data are used throughout De Boeck and Wilson, Explanatory Item Response Models (Springer, 2004) to illustrate various forms of item response models.

Format

A data frame with 7584 observations on the following 13 variables.

Anger

the subject's Trait Anger score as measured on the State-Trait Anger Expression Inventory (STAXI)

Gender

the subject's gender - a factor with levels M and F

item

the item on the questionaire, as a factor

resp

the subject's response to the item - an ordered factor with levels no < perhaps < yes

id

the subject identifier, as a factor

btype

behavior type - a factor with levels curse, scold and shout

situ

situation type - a factor with levels other and self indicating other-to-blame and self-to-blame

mode

behavior mode - a factor with levels want and do

r2

dichotomous version of the response - a factor with levels N and Y

Source

http://bear.soe.berkeley.edu/EIRM/

References

De Boeck and Wilson (2004), Explanatory Item Response Models, Springer.

Examples

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str(VerbAgg)
## Show how  r2 := h(resp) is defined:
with(VerbAgg, stopifnot( identical(r2, {
     r <- factor(resp, ordered=FALSE); levels(r) <- c("N","Y","Y"); r})))

xtabs(~ item + resp, VerbAgg)
xtabs(~ btype + resp, VerbAgg)
round(100 * ftable(prop.table(xtabs(~ situ + mode + resp, VerbAgg), 1:2), 1))
person <- unique(subset(VerbAgg, select = c(id, Gender, Anger)))
require(lattice)
densityplot(~ Anger, person, groups = Gender, auto.key = list(columns = 2),
            xlab = "Trait Anger score (STAXI)")

if(lme4:::testLevel() >= 3) { ## takes about 15 sec
    print(fmVA <- glmer(r2 ~ (Anger + Gender + btype + situ)^2 +
 		   (1|id) + (1|item), family = binomial, data =
		   VerbAgg), corr=FALSE)
} ## testLevel() >= 3
if (interactive()) {
## much faster but less accurate
    print(fmVA0 <- glmer(r2 ~ (Anger + Gender + btype + situ)^2 +
                             (1|id) + (1|item), family = binomial,
                         data = VerbAgg, nAGQ=0L), corr=FALSE)
} ## interactive()

Example output

Loading required package: Matrix
'data.frame':	7584 obs. of  9 variables:
 $ Anger : int  20 11 17 21 17 21 39 21 24 16 ...
 $ Gender: Factor w/ 2 levels "F","M": 2 2 1 1 1 1 1 1 1 1 ...
 $ item  : Factor w/ 24 levels "S1WantCurse",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ resp  : Ord.factor w/ 3 levels "no"<"perhaps"<..: 1 1 2 2 2 3 3 1 1 3 ...
 $ id    : Factor w/ 316 levels "1","2","3","4",..: 1 2 3 4 5 6 7 8 9 10 ...
 $ btype : Factor w/ 3 levels "curse","scold",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ situ  : Factor w/ 2 levels "other","self": 1 1 1 1 1 1 1 1 1 1 ...
 $ mode  : Factor w/ 2 levels "want","do": 1 1 1 1 1 1 1 1 1 1 ...
 $ r2    : Factor w/ 2 levels "N","Y": 1 1 2 2 2 2 2 1 1 2 ...
             resp
item           no perhaps yes
  S1WantCurse  91      95 130
  S1WantScold 126      86 104
  S1WantShout 154      99  63
  S2WantCurse  67     112 137
  S2WantScold 118      93 105
  S2WantShout 158      84  74
  S3WantCurse 128     120  68
  S3WantScold 198      90  28
  S3WantShout 240      63  13
  S4wantCurse  98     127  91
  S4WantScold 179      88  49
  S4WantShout 217      64  35
  S1DoCurse    91     108 117
  S1DoScold   136      97  83
  S1DoShout   208      68  40
  S2DoCurse   109      97 110
  S2DoScold   162      92  62
  S2DoShout   238      53  25
  S3DoCurse   171     108  37
  S3DoScold   239      61  16
  S3DoShout   287      25   4
  S4DoCurse   118     117  81
  S4DoScold   181      91  44
  S4DoShout   259      43  14
       resp
btype     no perhaps  yes
  curse  873     884  771
  scold 1339     698  491
  shout 1761     499  268
           resp no perhaps yes
situ  mode                    
other want      38      30  32
      do        50      27  23
self  want      56      29  15
      do        66      23  10
Loading required package: lattice

lme4 documentation built on June 22, 2021, 9:07 a.m.